NatureDepPrint

NatureDepPrint is a self-reported static browser-based platform that visualizes firm-level dependence on ecosystem services using a dataset of 222,564 firm-year records across 117 countries from 2010 to 2023. The platform presents this data through interactive visualizations such as 21-bar fingerprints and radial profiles, allowing users to explore recorded company-nature dependence across 21 ecosystem-service dimensions.

The project was built by two individuals (Malek Itani, Dr. Mahmoud Arayssi) as part of a hackathon submission and is hosted on GitHub Pages. It distinguishes between "nature dependence" — which reflects recorded reliance on ecosystem services — and "nature-related risk," which involves additional factors not captured in the dataset.

The description states that NatureDepPrint is an interactive platform designed to help users understand how businesses depend on ecosystem services across 21 primary dimensions. It presents firm-year records as visual fingerprints, supports exploration of company profiles, and includes a self-assessment tool for companies not in the dataset.

• The platform uses a static website architecture hosted on GitHub Pages.
• It contains a subset of 222,564 firm-year records from a larger dataset covering 31,772 listed firms across 117 countries from 2010 to 2023.
• Each company-year profile is represented as a fixed-order, fixed-scale 21-dimensional vector.
• Visualizations include vertical bar fingerprints, radial profiles, ranked scores, and circular/spider graphs.
• A self-assessment builder allows users to create a fingerprint based on potential functionality loss and adaptation difficulty.

Inference The platform is built for educational or exploratory purposes rather than operational decision-making. It does not predict risk or financial outcomes but aims to surface where deeper assessment may be needed.

The description states that NatureDepPrint was inspired by the question: “How can we make firm-nature dependence visible?” The platform positions itself as a way to transform complex academic research into an accessible, interactive experience for students, educators, and sustainability professionals.

It explicitly distinguishes between:

• Nature dependence, which reflects recorded reliance on ecosystem services.
• Nature-related risk, which includes factors not captured in the dataset such as location, disruption likelihood, and adaptation capacity.

The platform also emphasizes that it does not predict future events or financial losses — it helps identify where deeper risk assessment should begin.

Inference The positioning is educational and exploratory, not commercial or predictive. It frames itself as a tool for understanding rather than assessing or mitigating risk.

The description states that NatureDepPrint targets:

• Students
• Educators
• Sustainability professionals
• Business decision-makers

It also notes that the platform is designed to help users "understand recorded company dependence" and identify where deeper risk assessment may be useful.

There is no evidence of specific customer segments or personas beyond these general categories. The description does not indicate whether any of these groups have actually used the tool or are engaged with it.

Inference The ICP appears to be broad, encompassing academic and educational users, as well as professionals interested in sustainability. No clear segmentation or user validation is evident.

The description does not state anything about a business model or pricing structure for NatureDepPrint.

It is described as a static website hosted on GitHub Pages, with no mention of monetization, subscriptions, or paid features.

Inference There is no evidence of any revenue-generating mechanism. The project appears to be non-commercial in nature.

The platform was built using:

• HTML5
• JavaScript
• Python
• SVG
• CSV
• JSON
• GitHub Pages
• Encore (for self-assessment methodology)

It is a static browser-based website, preprocessed to support large datasets without backend or API dependencies.

The dataset is described as containing:

• 222,564 firm-year records
• 26,195 firms identified by unique RICs
• Data from 2010 to 2023
• 21 primary ecosystem-service dimensions

Visualizations are designed to be accessible and comparable across different views (bar, radial, ranked, etc.).

Inference The technical approach is lightweight and self-contained, suitable for educational or exploratory use. It avoids backend complexity but may limit scalability or interactivity.

The description states that the platform was built as part of a hackathon submission (OpenAI 2026) and has no evidence of:

• Revenue
• Customers
• User engagement
• Adoption
• Product-market fit

It is described as a prototype or proof-of-concept, not a production-ready product.

Inference There is no traction or maturity signal beyond its hackathon origin. No data on usage, retention, or impact is provided.

The description does not mention any direct competitors for NatureDepPrint. It is positioned as a tool that visualizes firm-nature dependence using a specific dataset and methodology derived from academic research.

It distinguishes itself by:

• Presenting dependence data in an interactive format
• Separating recorded scores from self-assessed scores
• Avoiding claims of risk prediction or financial forecasting

No mention of similar tools, platforms, or datasets in the market is provided.

Inference The competitive landscape is unclear. It may be a niche educational tool with no known direct competitors, but this cannot be confirmed without external data.

• Lack of commercial viability: No evidence of revenue, customers, or monetization.
• Limited scope and impact: Built as a hackathon project; no indication of long-term development or user engagement.
• No predictive capability: The platform explicitly avoids making risk predictions, which may limit its utility for decision-makers.
• Self-reported data only: No independent verification of the dataset or methodology.
• Static architecture limits scalability: Hosting on GitHub Pages and lack of backend implies limited interactivity or data updates.

Inference The project is likely a prototype with no commercial traction. It may not be suitable for investment or partnership unless further development and validation occur.

1. What is the source of the dataset, and how was it cleaned or processed?
2. Has the platform been tested or used by any target users (students, educators, professionals)?
3. Are there plans to expand beyond the current 21-service dimensions or integrate additional data sources?
4. How does the self-assessment tool differ from the official methodology in the research paper?
5. Is there any intention to monetize or scale this platform beyond its current form?
6. What are the long-term goals for the project, and how do they align with potential users' needs?